A numerical investigation of the influence of land-sea breeze on the atmospheric effluents released at a coastal site

ABSTRACT. Mesoscale features of a coastal atmospheric boundary layer such as the land-sea circulation and the thermal internal boundary layer (TIBL) structure have been simulated using a two-dimensional numerical boundary layer model. Using Boussinesq approximation for horizontal momentum equations and hydrostatic approximation for vertical momentum equation the model solves the 'shallow water' equations year over a grid domain 80 km length on either side of the coastline and 2 km height. The influence of the land-sea breezes on the dispersion of pollutants released from a continuous point source located at the roast has been studied. The fumigation of pollutants from an offshore source into TIBL over the land has also been illustrated. The limitations associated with the model are also discussed.

The Application of Wearable Glucose Sensors in Point-of-Care Testing

Diabetes and its complications have become a worldwide concern that influences human health negatively and even leads to death. The real-time and convenient glucose detection in biofluids is urgently needed. Traditional glucose testing is detecting glucose in blood and is invasive, which cannot be continuous and results in discomfort for the users. Consequently, wearable glucose sensors toward continuous point-of-care glucose testing in biofluids have attracted great attention, and the trend of glucose testing is from invasive to non-invasive. In this review, the wearable point-of-care glucose sensors for the detection of different biofluids including blood, sweat, saliva, tears, and interstitial fluid are discussed, and the future trend of development is prospected.

Python Package abstcal: An Open-Source Tool for Calculating Abstinence from Timeline Followback Data

In smoking cessation clinical trials, timeline followback (TLFB) interviews are widely used to track daily cigarette consumption. However, there are no standard tools for calculating abstinence based on TLFB data. Individual research groups have to develop their own calculation tools, which is not only time- and resource-consuming but might also lead to variability in the data processing and calculation procedures. To address these issues, we developed a novel open-source Python package named abstcal to calculate abstinence using TLFB data. This package provides data verification, duplicate and outlier detection, missing-data imputation, integration of biochemical verification data, and calculation of a variety of definitions of abstinence, including continuous, point-prevalence, and prolonged abstinence. We verified the accuracy of the calculator using data derived from a clinical smoking cessation study. To improve the package’s accessibility, we have made it available as a free web app. The abstcal package is a reliable abstinence calculator with open-source access, providing a shared validated online tool to the addiction research field. We expect that this open-source abstinence calculation tool will improve the rigor and reproducibility of smoking and addiction research by standardizing TLFB-based abstinence calculation.

Tracking via Volunteered Geographic Information

Over the past 20 years, the use of location-based tracking has become increasingly popular. The introduction of GPS technology into devices such as phones and watches, and its incorporation into tracking apps, has led to widespread use of apps which track activities, particularly those of a sporting nature. There are now over 318,000 health and fitness apps – called mHealth apps (Byambasuren et al., 2018) – and it is estimated that 75% of runners now use them (Janssen et al., 2017). Many of these apps contain the ability for users to track their movement and share it with fellow app users – Strava alone has 42 million accounts with 1 million users each month (Haden, 2019), but others include MapMyFitness, Adidas Running, and Google Fit. Importantly for this book, the data that is produced from mHealth apps is continuous point geo-referenced data that is visualised for the user as a defined route undertaken during a particular activity. This route, and the temporal and spatial aspects of the activity, can be viewed by the user and then released online for their online network to view. Most commonly, it is referred to as volunteered geographic information (VGI). The data that is generated from mHealth apps can be sourced by researchers; this is often referred to as crowd sourcing. Researchers can gather large amounts of data of entire paths taken by individual users, either via gaining consent from individual users to share their routes, or via APIs provided by the app developer which provide access to large amounts of routes and their associated statistics. VGI provides researchers with great potential to facilitate research that assesses tourists’ movement through space and time (Heikinheimo et al., 2017). However, as is the case with single point geo-referenced data (discussed in the previous chapter), research in this space is disparate and tends to focus on one platform at a time, or one context at a time. The rapid increase in VGI is arguably due to three factors: developments in wearable technology; developments in location based technology that has been integrated into smart phone and watch apps; and an increase in usage of urban spaces for walking, running and biking. The latter is largely due to an increased interest in healthy lifestyles and exercise (Santos et al., 2016; Brown et al., 2014) and presents issues for park managers, including those related to environmental impacts due to overuse and conflicts between different types of users, such as walkers and bike riders (Santos et al., 2016; Norman and Pickering, 2017; Pickering et al., 2011; Rossi et al., 2013). This chapter will explore how VGI data can assist researchers and managers in understanding these issues, along with tourists’ mobility.